class_docstr: Input transformed kernel.
  
  Given a kernel `k` and function `f`, compute `k_{new}(x, y) = k(f(x), f(y))`.
  
  
  ### Examples
  
  ```python
  import tensorflow as tf
  import tensorflow_probability as tfp
  from tensorflow_probability.positive_semidefinite_kernel.internal import util
  tfpk = tfp.math.psd_kernels
  
  base_kernel = tfpk.ExponentiatedQuadratic(amplitude=2., length_scale=1.)
  ```
  
  - Identity function.
  
  ```python
  # This is the same as base_kernel
  same_kernel = tfpk.FeatureTransformed(
      base_kernel,
      transformation_fn=lambda x, _, _: x)
  ```
  
  - Exponential transformation.
  
  ```python
  exp_kernel = tfpk.FeatureTransformed(
      base_kernel,
      transformation_fn=lambda x, _, _: tf.exp(x))
  ```
  
  - Transformation with broadcasting parameters.
  
  ```python
  # Exponentiate inputs
  
  p = np.random.uniform(low=2., high=3., size=[10, 2])
  def inputs_to_power(x, feature_ndims, param_expansion_ndims):
    # Make sure we account for extra feature dimensions for
    # broadcasting purposes.
    power = util.pad_shape_with_ones(
        p,
        ndims=feature_ndims + param_expansion_ndims,
        start=-(feature_ndims + 1))
    return x ** power
  
  power_kernel = tfpk.FeatureTransformed(
    base_kernel, transformation_fn=inputs_to_power)
docstr: Function that preprocesses inputs before handing them to `kernel`.
docstr_tok: ["Function","that","preprocesses","inputs","before","handing","them","to","`","kernel","`","."]
code: def transformation_fn(self):
      """Function that preprocesses inputs before handing them to `kernel`."""
      return self._transformation_fn
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/feature_transformed.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.math.psd_kernels import positive_semidefinite_kernel
label_desc: Use image node_id 6 for calling the FeatureTransformed obj's underlying member method code with example usage: obj.transformation_fn() and returns: self